A user-centric evaluation framework for recommender systems

By Pearl Huan Z. Pu, Li Chen, Rong Hu. ACM Conference on Recommender Systems, 2011

936 citations recorded by the source corpus.

Subfields: Information Systems, Information Systems and Management, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/a96898490180c86b63eee2e801de6e25de5aa71d

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Citations in the corpus

Explaining the user experience of recommender systems (2012, background): “An integrated view on the user experience of recommender systems can be obtained by means of user-centric development (McNee et al. 2006b) and evaluation (Pu and Chen 2010; Pu et al. 2012).”
A Survey on Conversational Recommender Systems (2020, background): “review shows that a wide range of different evaluation methodologies and metrics are used to evaluate CRS. In principle, general user-centric evaluation frameworks for recommender systems as proposed [108] and [66] can be applied for CRS as well. So far, however, while user-centric evaluation is com”
Evaluating Recommender Systems with User Experiments (2015, methodology): “framework is a classification of recommender system related constructs under these higher level concepts (inspired by related analysis-type frameworks of recommender system aspects [84, 95, 122]).”
Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities (2016, background): “Such mixed-initiative approaches are also promising to address other issues of recommender systems, such as increasing diversity (Hu and Pu, 2011) and novelty (Herlocker et al., 2004) of recommended results, and their deployment in high-risk application domains such as health-care and financing…”
Multistakeholder recommendation: Survey and research directions (2020, background): “Yet, research has conﬁrmed that users prefer diverse recommendation lists (Pu et al. 2011), a factor in tension with accuracy-based estimates of user utility.”
Short-Term Satisfaction and Long-Term Coverage: Understanding How Users Tolerate Algorithmic Exploration (2018, methodology): “We mainly follow the user-centric paradigm [26, 30, 36] for the evaluation of recommender systems in this paper.”
Research-paper recommender systems: a literature survey (2015, background): “Many papers about various aspects of recommender-system evaluation have been published [227], [269], [276], [281], [284], [299–301].”
Recommender systems based on user reviews: the state of the art (2015, methodology): “In designing and implementing user evaluation, the recently developed user-centered evaluation frameworks for RS could be referential (Knijnenburg et al. 2012; Pu et al. 2011).”
News recommender systems - Survey and roads ahead (2018, background): “In recent years, however, recommender systems research in a variety of domains has shown that it is also important to evaluate recommendations in a more user- or system utility-oriented way [48, 78, 83, 96, 151].”
Developing a Price-Sensitive Recommender System to Improve Accuracy and Business Performance of Ecommerce Applications (2015, background): “For instance, Pu et al. 11 found that ease of use and perceived usefulness is important for usage intentions, while trust and choice confidence are crucial for purchasing intentions.”
Evaluating recommender systems from the user’s perspective: survey of the state of the art (2012, methodology): “In Pu et al. (2009), an interface design method was described to increase users’ perception of recommendation diversity by grouping and categorizing any result set into an organization interface.”
Auralist: introducing serendipity into music recommendation (2012, background): “A limited proportion of such recommendations has been shown [23, 18] to have a positive, trust-building impact on user satisfaction, but it can also be seen that to be useful a recommender needs to suggest previously unknown items.”
Offline and online evaluation of news recommender systems at swissinfo.ch (2014, background): “The layout of the page is extremely important [16, 4].”
DualDiv: diversifying items and explanation styles in explainable hybrid recommendation (2019, background): “However, it has been recognized that the diversity of items in the recommended list is also an important factor for increasing users’ satisfaction with RSs because diversified recommended items allow users to browse a wider range of item types [9, 18].”
Recommendation systems: Principles, methods and evaluation (2015, background): “In e-commerce setting, recommender systems enhance revenues, for the fact that they are effective means of selling more products [3].”
Captivating algorithms: Recommender systems as traps (2018, background): “New ‘user-centered’ studies that sought to measure satisfaction through survey instruments found a striking result: beyond a certain point, improvements in RMSE did not correlate with increased user satisfaction (Knijnenburg et al., 2012; see also Pu et al., 2011).”
Diversity, Serendipity, Novelty, and Coverage (2016, background): “Serendipity has been reported to be difficult to explain to users [Said et al. 2013] or has been left out of the studies as being too similar to novelty [Pu et al. 2011].”